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2018年智慧教育与人工智能发展学术会议

作者： Xiaoyuan Xu School of Information Mechanical & Electrical Engineering Jiangsu Open University

Aiming at the problem that the traditional classification algorithm has low classification accuracy,a KNN classification algorithm based on STORM big data is *** giving the basic information of the classification algorithm,extract the data features,propose a description of the improved KNN classification method,introduce STORM big data,and optimize the KNN classification *** experimental results show that the improved algorithm has higher classification accuracy and has certain advantages.

关键词： Big data Storm K-nearest neighbor classification algorithm

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Improved classification algorithm Based on Genetic Programming and Its Application in Process Monitoring of Additive Manufacturing 24

Improved Classification Algorithm Based on Genetic Programmi...

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24th ISPE Inc International Conference on Transdisciplinary Engineering (TE) Focusing on Collaborative Systems in Transdisciplinary Engineering

作者： Yang, Zhensheng Huang, Youfang Shanghai Maritime Univ Coll Logist Engn Shanghai 201306 Peoples R China Shanghai Maritime Univ Minist Educ Engn Res Ctr Container Supply Chain Technol Shanghai 201306 Peoples R China

ISBN: (纸本)9781614997795;9781614997788

This work presented a defect classification methods based on improved classification algorithm in additive manufacturing process. To make the algorithm be applicable in process monitoring tasks, a method of optimizing the evolution process in GP evolution was raised in this work. A series of specific functions and their linear combinations were introduced to represent the GP classification model. The evolution process in this strategy is designed to optimize the coefficients of these functions and the offset. The advantaged in GP are also completely inherited. Comparing with GP alone, the improved strategy could reach higher classification accuracy in engineering application, i.e., process monitoring of additive manufacture.

关键词： classification algorithm Genetic Programming Additive manufacture Process monitoring

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Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data

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SURVEY REVIEW 2016年第351期48卷 430-437页

作者： Jenicka, S. Suruliandi, A. Einstein Coll Engn Comp Sci & Engn Tirunelveli Tamil Nadu India Manonmaniam Sundaranar Univ Dept Informat Technol Tirunelveli 627012 Tamil Nadu India

Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%.

关键词： Cartography classification algorithm classification accuracy Distributed algorithms Hidden Markov models Image segmentation Image texture analysis

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Research on classification algorithm in Data Mining

Research on Classification Algorithm in Data Mining

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2017 3rd International Conference on Environment,Biology,Medicine and Computer Applications(ICEBMCA 2017)

作者： Liu Caili Xi’An International University Institute Of Technology Department Of Computer Science And Technology

With the continuous development of information technology and computer industry, data processing has become a top priority. We want to do a good job of data processing, it is necessary to apply to the data classification algorithm, which as a key technology in data mining can be a good job to complete the data processing. In this paper, by comparing several different data classification algorithms, to find their similarities and differences to further promote the data classification algorithm to lay the foundation

关键词： classification algorithm Data Mining

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An Improved KNN classification algorithm based on Sampling

An Improved KNN Classification Algorithm based on Sampling

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2017 International Conference on Advances in Materials,Machinery,Electrical Engineering(AMMEE 2017)

作者： Zhiwei Cheng Caisen Chen Xuehuan Qiu Huan Xie The Academy of Armored Forces Engineering

ISBN: (纸本)9781510842915

K nearest neighbor（KNN） algorithm has been widely used as a simple and effective classification algorithm. The traditional KNN classification algorithm will find k nearest neighbors, it is necessary to calculate the distance from the test sample to all training samples. When the training sample data is very large, it will produce a high computational overhead, resulting in a decline in classification speed. Therefore, we optimize the distance calculation of the KNN algorithm. Since KNN only considers the k samples of the shortest distance from the test sample to the nearest training sample point, the large distance training has no effect on the classification of the algorithm. The improved method is to sample the training data around the test data, which reduces the number of distance calculation of the test data to each training data, and reduces the time complexity of the algorithm. The experimental results show that the optimized KNN classification algorithm is superior to the traditional KNN algorithm.

关键词： KNN classification algorithm computational overhead sampling

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Development of classification algorithm for Epileptic Seizures Using Electrocardiogram Signal

Development of Classification Algorithm for Epileptic Seizur...

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IEEE Annual India Conference (INDICON)

作者： Valke, Nikita S. Karthikeyan, B. R. MS Ramaiah Univ Appl Sci Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781509036462

An epilepsy classification system using electrocardiogram (ECG) data will ease the process of diagnosis. In epileptic patients, the seizures affect Heart Rate Variability (HRV). This emphasizes the importance of autonomic function changes in diagnosing epilepsy. The present work proposes an algorithm that classifies a person as epileptic or nonepileptic using ECG signal. Time Domain Features (TDF) and Frequency Domain Features (FDF), derived from the R-R Intervals (RRI) of ECG signal are utilized. In addition, Statistical Features (SF) are derived from extracted TDF and FDF. The Support Vector Machines (SVM) classifier is used to classify the ECG signal as epileptic or nonepileptic based on the extracted TDF, FDF and SF. The classification accuracy of the proposed method exhibits 97.5%. Analysis on clinical data shows that the proposed combination of TDF, FDF and statistical HRV features gives excellent classification accuracy. These results indicate that the proposed method can be applied to wearable heart rate measuring devices for diagnostic purpose.

关键词： biomedical signal processing classification algorithm epilepsy feature extraction

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A Hybrid classification algorithm Approach for Breast Cancer Diagnosis

A Hybrid Classification Algorithm Approach for Breast Cancer...

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IEEE Conference on Industrial Electronics and Applications

作者： Abed, Baraa M. Shaker, Khalid Jalab, Hamid A. Shaker, Hothefa Mansoor, Ali Mohammed Alwan, Ahmad F. Al-Gburi, Ihsan Salman Univ Anbar Coll Comp Sci & Informat Technol Ramadi Iraq Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Modern Coll Business & Sci Muscat Oman Univ Nizwa Coll Arts & Sci Dept Math & Phys Sci Nizwa Oman Istanbul Kemerburgaz Univ Grad Sch Sci & Engn Istanbul Turkey

ISBN: (纸本)9781509009251

Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.

关键词： Breast Cancer Diagnosis classification algorithm Genetic algorithm and k Nearest Neighbor algorithm

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English Language Statistical Machine Translation Oriented classification algorithm

English Language Statistical Machine Translation Oriented Cl...

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International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS)

作者： Yan, Jia Chao, Wang Mudanjiang Normal Univ Heilongjiang 157011 Mudanjiang Peoples R China

ISBN: (纸本)9781509004645

In this paper, we study on the problem of statistical machine translation (SMT) for English language, and the main innovation is that we introduce the SVM classifier to solve this problem. Particularly, the process of statistical machine translation is constructed of two modules, that is, 1) constructing the target language sentence, and 2) translating from target texts to source texts language. Afterwards, framework of the statistical machine translation system is provided. The main idea of statistical machine translation is in that the sentence with highest probability is selected by optimizing an objective function, which describes the relationships between source language sentences and target language sentences. Finally, experimental results demonstrate that our proposed algorithm outperforms Topic-Based SMT and Rule Based SMT.

关键词： Statistical machine translation classification algorithm English translation SVM Objective function

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Extension-based Recommendation System about classification algorithm and Its Application

Extension-based Recommendation System about Classification A...

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Yang, Jie Gao, Hong Liu, Wei Dalian Maritime Univ Transportat Management Coll 1Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Dept Math 1Linghai Rd Dalian 116026 Peoples R China

ISBN: (纸本)9781509030712

The purpose of this study is to propose an approach to recommend classification algorithms for real-world classification problems. First, the extension rhombus thinking mode is used to construct performance indicators of the classification algorithm. Second, an extension-based recommendation method about classification algorithm is proposed. Third, a recommendation system is designed to implement our recommendation method. The system input is divided into two parts: expert knowledge and user preference. In order to reduce user workloads and confusions about parameter settings, a real-time interaction progressive strategy is proposed to help users complete their inputs. Additionally, the case function is designed so that users can get more support information from current and previous cases. At last, an application is used to verify the effectiveness of our system. And the application result shows that our system can provide an efficient and intelligent decision support for users.

关键词： classification algorithm Recommendation system Decision support Extension rhombus thinking mode Extension-based innovation method

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Development of classification algorithm for Epileptic Seizures Using Electrocardiogram Signal

Development of Classification Algorithm for Epileptic Seizur...

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IEEE Annual India Conference

作者： Nikita S. Valke B. R. Karthikevan Department of Electronic and Communication Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (纸本)9781509036479

关键词： biomedical signal processing classification algorithm epilepsy feature extraction

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